Overview

Dataset statistics

Number of variables23
Number of observations1563215
Missing cells2595087
Missing cells (%)7.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory274.3 MiB
Average record size in memory184.0 B

Variable types

Numeric6
Categorical7
Text6
DateTime3
Boolean1

Alerts

SOURCE is highly imbalanced (72.5%)Imbalance
DEPARTMENT is highly imbalanced (55.9%)Imbalance
STATUS is highly imbalanced (94.2%)Imbalance
COUNTY is highly imbalanced (74.4%)Imbalance
DAYS TO CLOSE has 26515 (1.7%) missing valuesMissing
NEIGHBORHOOD has 46106 (2.9%) missing valuesMissing
COUNTY has 66959 (4.3%) missing valuesMissing
POLICE DISTRICT has 35265 (2.3%) missing valuesMissing
CATEGORY2 has 1001657 (64.1%) missing valuesMissing
CATEGORY3 has 1404943 (89.9%) missing valuesMissing
ZIP CODE is highly skewed (γ1 = -832.06849)Skewed
PARCEL ID NO is highly skewed (γ1 = 250.9088658)Skewed
CASE ID has unique valuesUnique
DAYS TO CLOSE has 150614 (9.6%) zerosZeros
PARCEL ID NO has 35266 (2.3%) zerosZeros

Reproduction

Analysis started2024-02-28 09:25:25.068614
Analysis finished2024-02-28 09:34:12.120086
Duration8 minutes and 47.05 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

CASE ID
Real number (ℝ)

UNIQUE 

Distinct1563215
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0140133 × 109
Minimum2.006 × 109
Maximum2.0210337 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.9 MiB
2024-02-28T04:34:12.460016image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum2.006 × 109
5-th percentile2.0071832 × 109
Q12.0101856 × 109
median2.0140975 × 109
Q32.0180632 × 109
95-th percentile2.0200896 × 109
Maximum2.0210337 × 109
Range15033684
Interquartile range (IQR)7877594

Descriptive statistics

Standard deviation4222588.7
Coefficient of variation (CV)0.0020966042
Kurtosis-1.3391166
Mean2.0140133 × 109
Median Absolute Deviation (MAD)3950638
Skewness-0.072037995
Sum3.1483358 × 1015
Variance1.7830256 × 1013
MonotonicityNot monotonic
2024-02-28T04:34:12.845373image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2019119972 1
 
< 0.1%
2015045877 1
 
< 0.1%
2015040302 1
 
< 0.1%
2015041682 1
 
< 0.1%
2015049038 1
 
< 0.1%
2015034811 1
 
< 0.1%
2015041579 1
 
< 0.1%
2015042453 1
 
< 0.1%
2015052481 1
 
< 0.1%
2015043953 1
 
< 0.1%
Other values (1563205) 1563205
> 99.9%
ValueCountFrequency (%)
2006000002 1
< 0.1%
2006000004 1
< 0.1%
2007000001 1
< 0.1%
2007000005 1
< 0.1%
2007000009 1
< 0.1%
2007000010 1
< 0.1%
2007000012 1
< 0.1%
2007000013 1
< 0.1%
2007000014 1
< 0.1%
2007000015 1
< 0.1%
ValueCountFrequency (%)
2021033686 1
< 0.1%
2021033407 1
< 0.1%
2021033233 1
< 0.1%
2021033140 1
< 0.1%
2021032869 1
< 0.1%
2021032828 1
< 0.1%
2021032612 1
< 0.1%
2021032575 1
< 0.1%
2021032525 1
< 0.1%
2021032480 1
< 0.1%

SOURCE
Categorical

IMBALANCE 

Distinct21
Distinct (%)< 0.1%
Missing67
Missing (%)< 0.1%
Memory size11.9 MiB
PHONE
1204236 
WEB
211721 
EMAIL
 
80585
SYS
 
19226
INSPE
 
14690
Other values (16)
 
32690

Length

Max length5
Median length5
Mean length4.677877
Min length3

Characters and Unicode

Total characters7312214
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPHONE
2nd rowWEB
3rd rowPHONE
4th rowPHONE
5th rowWEB

Common Values

ValueCountFrequency (%)
PHONE 1204236
77.0%
WEB 211721
 
13.5%
EMAIL 80585
 
5.2%
SYS 19226
 
1.2%
INSPE 14690
 
0.9%
BOT 13396
 
0.9%
TWIR 8311
 
0.5%
VOICE 6021
 
0.4%
WALK 1792
 
0.1%
FAX 1538
 
0.1%
Other values (11) 1632
 
0.1%

Length

2024-02-28T04:34:13.208129image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
phone 1204236
77.0%
web 211721
 
13.5%
email 80585
 
5.2%
sys 19226
 
1.2%
inspe 14690
 
0.9%
bot 13396
 
0.9%
twir 8311
 
0.5%
voice 6021
 
0.4%
walk 1792
 
0.1%
fax 1538
 
0.1%
Other values (11) 1632
 
0.1%

Most occurring characters

ValueCountFrequency (%)
E 1518427
20.8%
O 1223712
16.7%
P 1219913
16.7%
N 1218950
16.7%
H 1204260
16.5%
B 225129
 
3.1%
W 221824
 
3.0%
I 110312
 
1.5%
A 84238
 
1.2%
L 82700
 
1.1%
Other values (12) 202749
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7312214
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 1518427
20.8%
O 1223712
16.7%
P 1219913
16.7%
N 1218950
16.7%
H 1204260
16.5%
B 225129
 
3.1%
W 221824
 
3.0%
I 110312
 
1.5%
A 84238
 
1.2%
L 82700
 
1.1%
Other values (12) 202749
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 7312214
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 1518427
20.8%
O 1223712
16.7%
P 1219913
16.7%
N 1218950
16.7%
H 1204260
16.5%
B 225129
 
3.1%
W 221824
 
3.0%
I 110312
 
1.5%
A 84238
 
1.2%
L 82700
 
1.1%
Other values (12) 202749
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7312214
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 1518427
20.8%
O 1223712
16.7%
P 1219913
16.7%
N 1218950
16.7%
H 1204260
16.5%
B 225129
 
3.1%
W 221824
 
3.0%
I 110312
 
1.5%
A 84238
 
1.2%
L 82700
 
1.1%
Other values (12) 202749
 
2.8%

DEPARTMENT
Categorical

IMBALANCE 

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.9 MiB
NHS
783094 
Public Works
353787 
Water Services
216852 
Parks and Rec
87954 
Health
 
39543
Other values (22)
81985 

Length

Max length35
Median length3
Mean length7.6318446
Min length2

Characters and Unicode

Total characters11930214
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowNHS
2nd rowPublic Works
3rd rowNHS
4th rowNHS
5th rowParks and Rec

Common Values

ValueCountFrequency (%)
NHS 783094
50.1%
Public Works 353787
22.6%
Water Services 216852
 
13.9%
Parks and Rec 87954
 
5.6%
Health 39543
 
2.5%
KCPD 36369
 
2.3%
City Managers Office 13098
 
0.8%
City Planning and Development 12575
 
0.8%
Northland 8591
 
0.5%
NCS 6391
 
0.4%
Other values (17) 4961
 
0.3%

Length

2024-02-28T04:34:13.503016image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nhs 783094
32.9%
public 353787
14.9%
works 353787
14.9%
water 216852
 
9.1%
services 216852
 
9.1%
and 100588
 
4.2%
parks 88826
 
3.7%
rec 88815
 
3.7%
health 39543
 
1.7%
kcpd 36369
 
1.5%
Other values (32) 98741
 
4.2%

Most occurring characters

ValueCountFrequency (%)
S 1007164
 
8.4%
r 900261
 
7.5%
e 847896
 
7.1%
H 822980
 
6.9%
814039
 
6.8%
N 798111
 
6.7%
c 675143
 
5.7%
s 672981
 
5.6%
i 626675
 
5.3%
W 570639
 
4.8%
Other values (30) 4194325
35.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7151431
59.9%
Uppercase Letter 3963872
33.2%
Space Separator 814039
 
6.8%
Other Punctuation 872
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 900261
12.6%
e 847896
11.9%
c 675143
9.4%
s 672981
9.4%
i 626675
8.8%
a 496002
 
6.9%
k 442615
 
6.2%
l 427997
 
6.0%
o 376777
 
5.3%
u 355543
 
5.0%
Other values (11) 1329541
18.6%
Uppercase Letter
ValueCountFrequency (%)
S 1007164
25.4%
H 822980
20.8%
N 798111
20.1%
W 570639
14.4%
P 491557
12.4%
R 88827
 
2.2%
C 69282
 
1.7%
D 49286
 
1.2%
K 36369
 
0.9%
M 13514
 
0.3%
Other values (7) 16143
 
0.4%
Space Separator
ValueCountFrequency (%)
814039
100.0%
Other Punctuation
ValueCountFrequency (%)
& 872
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11115303
93.2%
Common 814911
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 1007164
 
9.1%
r 900261
 
8.1%
e 847896
 
7.6%
H 822980
 
7.4%
N 798111
 
7.2%
c 675143
 
6.1%
s 672981
 
6.1%
i 626675
 
5.6%
W 570639
 
5.1%
a 496002
 
4.5%
Other values (28) 3697451
33.3%
Common
ValueCountFrequency (%)
814039
99.9%
& 872
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11930214
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 1007164
 
8.4%
r 900261
 
7.5%
e 847896
 
7.1%
H 822980
 
6.9%
814039
 
6.8%
N 798111
 
6.7%
c 675143
 
5.7%
s 672981
 
5.6%
i 626675
 
5.3%
W 570639
 
4.8%
Other values (30) 4194325
35.2%
Distinct146
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.9 MiB
2024-02-28T04:34:13.931992image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length50
Median length47
Mean length31.048004
Min length6

Characters and Unicode

Total characters48534706
Distinct characters53
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowNHS-Dangerous Buildings-
2nd rowPublic Works-Street and Traffic-District 1
3rd rowNHS-Dangerous Buildings-
4th rowNHS-Neighborhood Preservation-
5th rowParks and Rec-Central Region-
ValueCountFrequency (%)
and 497876
 
10.5%
public 353787
 
7.5%
waste 337776
 
7.2%
nhs-neighborhood 317500
 
6.7%
nhs-solid 299322
 
6.3%
preservation 286854
 
6.1%
water 216852
 
4.6%
works-street 159398
 
3.4%
health 143813
 
3.0%
nhs-animal 139749
 
3.0%
Other values (206) 1968123
41.7%
2024-02-28T04:34:14.770933image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 4530059
 
9.3%
i 3300515
 
6.8%
r 3283333
 
6.8%
a 3180184
 
6.6%
3157835
 
6.5%
- 3121076
 
6.4%
t 2853937
 
5.9%
o 2635467
 
5.4%
s 2190723
 
4.5%
n 2099413
 
4.3%
Other values (43) 18182164
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 34166821
70.4%
Uppercase Letter 7973083
 
16.4%
Space Separator 3157835
 
6.5%
Dash Punctuation 3121076
 
6.4%
Decimal Number 114274
 
0.2%
Other Punctuation 1617
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4530059
13.3%
i 3300515
9.7%
r 3283333
9.6%
a 3180184
9.3%
t 2853937
8.4%
o 2635467
 
7.7%
s 2190723
 
6.4%
n 2099413
 
6.1%
l 1550002
 
4.5%
d 1511973
 
4.4%
Other values (14) 7031215
20.6%
Uppercase Letter
ValueCountFrequency (%)
S 2058471
25.8%
N 1136393
14.3%
W 1020241
12.8%
P 1005647
12.6%
H 976676
12.2%
C 262046
 
3.3%
A 220751
 
2.8%
M 215408
 
2.7%
T 213447
 
2.7%
L 202835
 
2.5%
Other values (13) 661168
 
8.3%
Decimal Number
ValueCountFrequency (%)
3 50862
44.5%
1 39053
34.2%
2 24359
21.3%
Space Separator
ValueCountFrequency (%)
3157835
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3121076
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1617
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 42139904
86.8%
Common 6394802
 
13.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4530059
 
10.8%
i 3300515
 
7.8%
r 3283333
 
7.8%
a 3180184
 
7.5%
t 2853937
 
6.8%
o 2635467
 
6.3%
s 2190723
 
5.2%
n 2099413
 
5.0%
S 2058471
 
4.9%
l 1550002
 
3.7%
Other values (37) 14457800
34.3%
Common
ValueCountFrequency (%)
3157835
49.4%
- 3121076
48.8%
3 50862
 
0.8%
1 39053
 
0.6%
2 24359
 
0.4%
& 1617
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48534706
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 4530059
 
9.3%
i 3300515
 
6.8%
r 3283333
 
6.8%
a 3180184
 
6.6%
3157835
 
6.5%
- 3121076
 
6.4%
t 2853937
 
5.9%
o 2635467
 
5.4%
s 2190723
 
4.5%
n 2099413
 
4.3%
Other values (43) 18182164
37.5%

TYPE
Text

Distinct295
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.9 MiB
2024-02-28T04:34:15.230143image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length48
Median length29
Mean length11.899895
Min length2

Characters and Unicode

Total characters18602094
Distinct characters69
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowDangerous Building
2nd rowCrack
3rd rowDangerous Building
4th rowProperty Maintenance
5th rowPark Maintenance
ValueCountFrequency (%)
property 246467
 
9.1%
private 213828
 
7.9%
118647
 
4.4%
street 99068
 
3.7%
trash 70159
 
2.6%
collection 70001
 
2.6%
maintenance 65340
 
2.4%
animal 52113
 
1.9%
stray 47867
 
1.8%
pothole 39777
 
1.5%
Other values (343) 1683117
62.2%
2024-02-28T04:34:16.008463image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1934335
 
10.4%
r 1626910
 
8.7%
t 1536231
 
8.3%
a 1346391
 
7.2%
i 1251603
 
6.7%
1143169
 
6.1%
n 1133790
 
6.1%
o 1009491
 
5.4%
l 663892
 
3.6%
P 578906
 
3.1%
Other values (59) 6377376
34.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14743571
79.3%
Uppercase Letter 2509188
 
13.5%
Space Separator 1143169
 
6.1%
Other Punctuation 125515
 
0.7%
Decimal Number 54301
 
0.3%
Dash Punctuation 13999
 
0.1%
Open Punctuation 6102
 
< 0.1%
Close Punctuation 6102
 
< 0.1%
Initial Punctuation 147
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1934335
13.1%
r 1626910
11.0%
t 1536231
10.4%
a 1346391
9.1%
i 1251603
 
8.5%
n 1133790
 
7.7%
o 1009491
 
6.8%
l 663892
 
4.5%
c 562616
 
3.8%
y 499008
 
3.4%
Other values (16) 3179304
21.6%
Uppercase Letter
ValueCountFrequency (%)
P 578906
23.1%
S 289650
11.5%
C 240537
9.6%
R 183614
 
7.3%
A 162665
 
6.5%
B 147822
 
5.9%
T 145769
 
5.8%
D 118884
 
4.7%
L 109082
 
4.3%
M 108032
 
4.3%
Other values (15) 424227
16.9%
Decimal Number
ValueCountFrequency (%)
1 14948
27.5%
2 11371
20.9%
0 8742
16.1%
3 3140
 
5.8%
6 2923
 
5.4%
8 2812
 
5.2%
4 2647
 
4.9%
7 2619
 
4.8%
9 2608
 
4.8%
5 2491
 
4.6%
Other Punctuation
ValueCountFrequency (%)
/ 116109
92.5%
& 9373
 
7.5%
. 33
 
< 0.1%
Space Separator
ValueCountFrequency (%)
1143169
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13999
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6102
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6102
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 147
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17252759
92.7%
Common 1349335
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1934335
 
11.2%
r 1626910
 
9.4%
t 1536231
 
8.9%
a 1346391
 
7.8%
i 1251603
 
7.3%
n 1133790
 
6.6%
o 1009491
 
5.9%
l 663892
 
3.8%
P 578906
 
3.4%
c 562616
 
3.3%
Other values (41) 5608594
32.5%
Common
ValueCountFrequency (%)
1143169
84.7%
/ 116109
 
8.6%
1 14948
 
1.1%
- 13999
 
1.0%
2 11371
 
0.8%
& 9373
 
0.7%
0 8742
 
0.6%
( 6102
 
0.5%
) 6102
 
0.5%
3 3140
 
0.2%
Other values (8) 16280
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18601947
> 99.9%
Punctuation 147
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1934335
 
10.4%
r 1626910
 
8.7%
t 1536231
 
8.3%
a 1346391
 
7.2%
i 1251603
 
6.7%
1143169
 
6.1%
n 1133790
 
6.1%
o 1009491
 
5.4%
l 663892
 
3.6%
P 578906
 
3.1%
Other values (58) 6377229
34.3%
Punctuation
ValueCountFrequency (%)
‘ 147
100.0%

DETAIL
Text

Distinct574
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.9 MiB
2024-02-28T04:34:16.450423image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length48
Median length31
Mean length10.467135
Min length3

Characters and Unicode

Total characters16362382
Distinct characters66
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)< 0.1%

Sample

1st rowStandard
2nd rowDistrict 1
3rd rowStandard
4th rowOther Property Issue
5th rowCentral
ValueCountFrequency (%)
all 428758
 
14.3%
missed 172087
 
5.8%
135089
 
4.5%
by 107464
 
3.6%
district 107235
 
3.6%
contractor 89601
 
3.0%
property 70100
 
2.3%
south 69653
 
2.3%
north 60495
 
2.0%
one 57947
 
1.9%
Other values (600) 1693151
56.6%
2024-02-28T04:34:17.291421image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1505379
 
9.2%
1428365
 
8.7%
t 1310932
 
8.0%
r 1212748
 
7.4%
l 1087247
 
6.6%
i 896135
 
5.5%
o 845654
 
5.2%
s 787339
 
4.8%
n 708151
 
4.3%
a 643131
 
3.9%
Other values (56) 5937301
36.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11901213
72.7%
Uppercase Letter 2711940
 
16.6%
Space Separator 1428365
 
8.7%
Dash Punctuation 114893
 
0.7%
Other Punctuation 108326
 
0.7%
Decimal Number 78896
 
0.5%
Open Punctuation 8339
 
0.1%
Close Punctuation 8339
 
0.1%
Math Symbol 2038
 
< 0.1%
Initial Punctuation 33
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1505379
12.6%
t 1310932
11.0%
r 1212748
10.2%
l 1087247
9.1%
i 896135
 
7.5%
o 845654
 
7.1%
s 787339
 
6.6%
n 708151
 
6.0%
a 643131
 
5.4%
c 431294
 
3.6%
Other values (16) 2473203
20.8%
Uppercase Letter
ValueCountFrequency (%)
A 510716
18.8%
S 295407
10.9%
M 234827
8.7%
C 234346
8.6%
D 189901
 
7.0%
P 175668
 
6.5%
N 159626
 
5.9%
L 151890
 
5.6%
O 134016
 
4.9%
T 120206
 
4.4%
Other values (15) 505337
18.6%
Decimal Number
ValueCountFrequency (%)
3 31985
40.5%
1 22084
28.0%
2 20851
26.4%
5 2038
 
2.6%
4 1938
 
2.5%
Other Punctuation
ValueCountFrequency (%)
/ 62947
58.1%
* 45286
41.8%
' 64
 
0.1%
. 29
 
< 0.1%
Space Separator
ValueCountFrequency (%)
1428365
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 114893
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8339
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8339
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2038
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14613153
89.3%
Common 1749229
 
10.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1505379
 
10.3%
t 1310932
 
9.0%
r 1212748
 
8.3%
l 1087247
 
7.4%
i 896135
 
6.1%
o 845654
 
5.8%
s 787339
 
5.4%
n 708151
 
4.8%
a 643131
 
4.4%
A 510716
 
3.5%
Other values (41) 5105721
34.9%
Common
ValueCountFrequency (%)
1428365
81.7%
- 114893
 
6.6%
/ 62947
 
3.6%
* 45286
 
2.6%
3 31985
 
1.8%
1 22084
 
1.3%
2 20851
 
1.2%
( 8339
 
0.5%
) 8339
 
0.5%
5 2038
 
0.1%
Other values (5) 4102
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16362349
> 99.9%
Punctuation 33
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1505379
 
9.2%
1428365
 
8.7%
t 1310932
 
8.0%
r 1212748
 
7.4%
l 1087247
 
6.6%
i 896135
 
5.5%
o 845654
 
5.2%
s 787339
 
4.8%
n 708151
 
4.3%
a 643131
 
3.9%
Other values (55) 5937268
36.3%
Punctuation
ValueCountFrequency (%)
‘ 33
100.0%
Distinct5229
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.9 MiB
Minimum2006-12-29 00:00:00
Maximum2021-10-28 00:00:00
2024-02-28T04:34:17.626090image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:34:17.980102image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1440
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.9 MiB
Minimum2024-02-28 00:00:00
Maximum2024-02-28 23:59:00
2024-02-28T04:34:18.304105image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:34:18.646514image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

STATUS
Categorical

IMBALANCE 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.9 MiB
RESOL
1535389 
CANC
 
13818
OPEN
 
12657
DUP
 
1301
ASSIG
 
45

Length

Max length5
Median length5
Mean length4.981396
Min length3

Characters and Unicode

Total characters7786993
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRESOL
2nd rowRESOL
3rd rowRESOL
4th rowRESOL
5th rowRESOL

Common Values

ValueCountFrequency (%)
RESOL 1535389
98.2%
CANC 13818
 
0.9%
OPEN 12657
 
0.8%
DUP 1301
 
0.1%
ASSIG 45
 
< 0.1%
FAIL 5
 
< 0.1%

Length

2024-02-28T04:34:18.980300image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T04:34:19.281747image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
resol 1535389
98.2%
canc 13818
 
0.9%
open 12657
 
0.8%
dup 1301
 
0.1%
assig 45
 
< 0.1%
fail 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E 1548046
19.9%
O 1548046
19.9%
S 1535479
19.7%
L 1535394
19.7%
R 1535389
19.7%
C 27636
 
0.4%
N 26475
 
0.3%
P 13958
 
0.2%
A 13868
 
0.2%
D 1301
 
< 0.1%
Other values (4) 1401
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7786993
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 1548046
19.9%
O 1548046
19.9%
S 1535479
19.7%
L 1535394
19.7%
R 1535389
19.7%
C 27636
 
0.4%
N 26475
 
0.3%
P 13958
 
0.2%
A 13868
 
0.2%
D 1301
 
< 0.1%
Other values (4) 1401
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 7786993
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 1548046
19.9%
O 1548046
19.9%
S 1535479
19.7%
L 1535394
19.7%
R 1535389
19.7%
C 27636
 
0.4%
N 26475
 
0.3%
P 13958
 
0.2%
A 13868
 
0.2%
D 1301
 
< 0.1%
Other values (4) 1401
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7786993
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 1548046
19.9%
O 1548046
19.9%
S 1535479
19.7%
L 1535394
19.7%
R 1535389
19.7%
C 27636
 
0.4%
N 26475
 
0.3%
P 13958
 
0.2%
A 13868
 
0.2%
D 1301
 
< 0.1%
Other values (4) 1401
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing23
Missing (%)< 0.1%
Memory size3.0 MiB
False
1079493 
True
483699 
(Missing)
 
23
ValueCountFrequency (%)
False 1079493
69.1%
True 483699
30.9%
(Missing) 23
 
< 0.1%
2024-02-28T04:34:19.537243image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Distinct4995
Distinct (%)0.3%
Missing12702
Missing (%)0.8%
Memory size11.9 MiB
Minimum2007-01-04 00:00:00
Maximum2022-02-11 00:00:00
2024-02-28T04:34:19.810140image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:34:20.157592image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

DAYS TO CLOSE
Real number (ℝ)

MISSING  ZEROS 

Distinct2748
Distinct (%)0.2%
Missing26515
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean51.533404
Minimum-21
Maximum4525
Zeros150614
Zeros (%)9.6%
Negative29
Negative (%)< 0.1%
Memory size11.9 MiB
2024-02-28T04:34:20.481467image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-21
5-th percentile0
Q11
median4
Q323
95-th percentile261
Maximum4525
Range4546
Interquartile range (IQR)22

Descriptive statistics

Standard deviation169.57318
Coefficient of variation (CV)3.2905488
Kurtosis77.017163
Mean51.533404
Median Absolute Deviation (MAD)3
Skewness7.3881016
Sum79191382
Variance28755.063
MonotonicityNot monotonic
2024-02-28T04:34:21.070472image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 322471
20.6%
0 150614
 
9.6%
2 120416
 
7.7%
3 103139
 
6.6%
4 77824
 
5.0%
5 57503
 
3.7%
6 49736
 
3.2%
7 44990
 
2.9%
8 31128
 
2.0%
9 20513
 
1.3%
Other values (2738) 558366
35.7%
(Missing) 26515
 
1.7%
ValueCountFrequency (%)
-21 17
 
< 0.1%
-20 2
 
< 0.1%
-19 8
 
< 0.1%
-18 1
 
< 0.1%
-1 1
 
< 0.1%
0 150614
9.6%
1 322471
20.6%
2 120416
 
7.7%
3 103139
 
6.6%
4 77824
 
5.0%
ValueCountFrequency (%)
4525 1
< 0.1%
4471 1
< 0.1%
4396 1
< 0.1%
4188 1
< 0.1%
4183 1
< 0.1%
4175 1
< 0.1%
4160 1
< 0.1%
4157 1
< 0.1%
4135 1
< 0.1%
4132 1
< 0.1%
Distinct277728
Distinct (%)17.8%
Missing24
Missing (%)< 0.1%
Memory size11.9 MiB
2024-02-28T04:34:21.569435image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length39
Median length37
Mean length15.80865
Min length4

Characters and Unicode

Total characters24711939
Distinct characters75
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique67349 ?
Unique (%)4.3%

Sample

1st row2317 E 50th St
2nd row10329 N Forest Ave
3rd row2623 E 10th St
4th row2474 Chelsea Ave
5th row400 W 31st St
ValueCountFrequency (%)
ave 602207
 
10.9%
st 476456
 
8.6%
e 327586
 
5.9%
n 180177
 
3.3%
ter 156915
 
2.8%
rd 131274
 
2.4%
ne 112479
 
2.0%
w 70394
 
1.3%
blvd 64833
 
1.2%
nw 64756
 
1.2%
Other values (12145) 3334294
60.4%
2024-02-28T04:34:22.466386image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3958210
 
16.0%
1 1284556
 
5.2%
0 1173280
 
4.7%
E 1116467
 
4.5%
e 909295
 
3.7%
A 892797
 
3.6%
2 785798
 
3.2%
t 762941
 
3.1%
T 726450
 
2.9%
3 717530
 
2.9%
Other values (65) 12384615
50.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7637289
30.9%
Decimal Number 7183875
29.1%
Lowercase Letter 5925190
24.0%
Space Separator 3958210
16.0%
Dash Punctuation 5688
 
< 0.1%
Other Punctuation 1677
 
< 0.1%
Open Punctuation 5
 
< 0.1%
Close Punctuation 5
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 1116467
14.6%
A 892797
11.7%
T 726450
 
9.5%
S 694753
 
9.1%
N 661679
 
8.7%
R 501664
 
6.6%
V 331386
 
4.3%
W 291712
 
3.8%
L 290404
 
3.8%
O 286483
 
3.8%
Other values (16) 1843494
24.1%
Lowercase Letter
ValueCountFrequency (%)
e 909295
15.3%
t 762941
12.9%
r 482556
8.1%
v 427188
 
7.2%
n 420855
 
7.1%
a 372413
 
6.3%
o 364188
 
6.1%
l 354868
 
6.0%
d 338821
 
5.7%
h 306003
 
5.2%
Other values (16) 1186062
20.0%
Decimal Number
ValueCountFrequency (%)
1 1284556
17.9%
0 1173280
16.3%
2 785798
10.9%
3 717530
10.0%
4 714711
9.9%
5 619841
8.6%
6 496732
 
6.9%
7 496633
 
6.9%
8 477639
 
6.6%
9 417155
 
5.8%
Other Punctuation
ValueCountFrequency (%)
' 1342
80.0%
, 100
 
6.0%
. 70
 
4.2%
/ 70
 
4.2%
# 68
 
4.1%
& 13
 
0.8%
" 10
 
0.6%
: 3
 
0.2%
% 1
 
0.1%
Space Separator
ValueCountFrequency (%)
3958210
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5688
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13562479
54.9%
Common 11149460
45.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 1116467
 
8.2%
e 909295
 
6.7%
A 892797
 
6.6%
t 762941
 
5.6%
T 726450
 
5.4%
S 694753
 
5.1%
N 661679
 
4.9%
R 501664
 
3.7%
r 482556
 
3.6%
v 427188
 
3.1%
Other values (42) 6386689
47.1%
Common
ValueCountFrequency (%)
3958210
35.5%
1 1284556
 
11.5%
0 1173280
 
10.5%
2 785798
 
7.0%
3 717530
 
6.4%
4 714711
 
6.4%
5 619841
 
5.6%
6 496732
 
4.5%
7 496633
 
4.5%
8 477639
 
4.3%
Other values (13) 424530
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24711939
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3958210
 
16.0%
1 1284556
 
5.2%
0 1173280
 
4.7%
E 1116467
 
4.5%
e 909295
 
3.7%
A 892797
 
3.6%
2 785798
 
3.2%
t 762941
 
3.1%
T 726450
 
2.9%
3 717530
 
2.9%
Other values (65) 12384615
50.1%

ZIP CODE
Real number (ℝ)

SKEWED 

Distinct65
Distinct (%)< 0.1%
Missing826
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean64126.843
Minimum4114
Maximum66203
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.9 MiB
2024-02-28T04:34:22.767679image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum4114
5-th percentile64109
Q164116
median64128
Q364133
95-th percentile64155
Maximum66203
Range62089
Interquartile range (IQR)17

Descriptive statistics

Standard deviation68.122948
Coefficient of variation (CV)0.0010623156
Kurtosis721225.59
Mean64126.843
Median Absolute Deviation (MAD)9
Skewness-832.06849
Sum1.0019107 × 1011
Variance4640.7361
MonotonicityNot monotonic
2024-02-28T04:34:23.086148image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64130 133154
 
8.5%
64127 90867
 
5.8%
64114 83166
 
5.3%
64134 77097
 
4.9%
64131 76437
 
4.9%
64132 76361
 
4.9%
64128 73073
 
4.7%
64110 64771
 
4.1%
64119 57224
 
3.7%
64111 50195
 
3.2%
Other values (55) 780044
49.9%
ValueCountFrequency (%)
4114 1
 
< 0.1%
6152 1
 
< 0.1%
63130 1
 
< 0.1%
64012 39
 
< 0.1%
64028 22
 
< 0.1%
64030 27
 
< 0.1%
64052 47
 
< 0.1%
64053 115
< 0.1%
64055 28
 
< 0.1%
64068 280
< 0.1%
ValueCountFrequency (%)
66203 1
 
< 0.1%
64444 15
 
< 0.1%
64167 194
 
< 0.1%
64166 460
 
< 0.1%
64165 486
 
< 0.1%
64164 522
 
< 0.1%
64163 1160
 
0.1%
64161 1936
 
0.1%
64160 89
 
< 0.1%
64158 6892
0.4%

NEIGHBORHOOD
Text

MISSING 

Distinct250
Distinct (%)< 0.1%
Missing46106
Missing (%)2.9%
Memory size11.9 MiB
2024-02-28T04:34:23.572335image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length42
Median length29
Mean length14.972397
Min length2

Characters and Unicode

Total characters22714758
Distinct characters60
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBlue Hills
2nd rowNew Mark
3rd rowIndependence Plaza
4th rowEast Community Team South
5th rowWestside South
ValueCountFrequency (%)
park 147748
 
4.3%
hills 128098
 
3.7%
east 108867
 
3.2%
creek 104581
 
3.0%
blue 98087
 
2.8%
south 98018
 
2.8%
north 94066
 
2.7%
and 77209
 
2.2%
shoal 59166
 
1.7%
oak 59067
 
1.7%
Other values (269) 2476354
71.8%
2024-02-28T04:34:24.452212image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 2024521
 
8.9%
1934152
 
8.5%
a 1649498
 
7.3%
o 1561057
 
6.9%
r 1370100
 
6.0%
t 1291903
 
5.7%
l 1174709
 
5.2%
n 1168235
 
5.1%
s 1134269
 
5.0%
i 1065864
 
4.7%
Other values (50) 8340450
36.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 17008571
74.9%
Uppercase Letter 3515573
 
15.5%
Space Separator 1934152
 
8.5%
Decimal Number 140924
 
0.6%
Dash Punctuation 82454
 
0.4%
Other Punctuation 33084
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2024521
11.9%
a 1649498
9.7%
o 1561057
9.2%
r 1370100
 
8.1%
t 1291903
 
7.6%
l 1174709
 
6.9%
n 1168235
 
6.9%
s 1134269
 
6.7%
i 1065864
 
6.3%
h 746484
 
4.4%
Other values (15) 3821931
22.5%
Uppercase Letter
ValueCountFrequency (%)
S 367459
 
10.5%
C 360417
 
10.3%
H 349438
 
9.9%
P 322701
 
9.2%
W 224225
 
6.4%
M 205260
 
5.8%
E 203178
 
5.8%
B 195938
 
5.6%
N 186385
 
5.3%
R 155777
 
4.4%
Other values (13) 944795
26.9%
Decimal Number
ValueCountFrequency (%)
6 37402
26.5%
9 25165
17.9%
4 25165
17.9%
3 25165
17.9%
2 13161
 
9.3%
7 10056
 
7.1%
1 2405
 
1.7%
8 2405
 
1.7%
Other Punctuation
ValueCountFrequency (%)
/ 19923
60.2%
& 13161
39.8%
Space Separator
ValueCountFrequency (%)
1934152
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 82454
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20524144
90.4%
Common 2190614
 
9.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2024521
 
9.9%
a 1649498
 
8.0%
o 1561057
 
7.6%
r 1370100
 
6.7%
t 1291903
 
6.3%
l 1174709
 
5.7%
n 1168235
 
5.7%
s 1134269
 
5.5%
i 1065864
 
5.2%
h 746484
 
3.6%
Other values (38) 7337504
35.8%
Common
ValueCountFrequency (%)
1934152
88.3%
- 82454
 
3.8%
6 37402
 
1.7%
9 25165
 
1.1%
4 25165
 
1.1%
3 25165
 
1.1%
/ 19923
 
0.9%
2 13161
 
0.6%
& 13161
 
0.6%
7 10056
 
0.5%
Other values (2) 4810
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22714758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2024521
 
8.9%
1934152
 
8.5%
a 1649498
 
7.3%
o 1561057
 
6.9%
r 1370100
 
6.0%
t 1291903
 
5.7%
l 1174709
 
5.2%
n 1168235
 
5.1%
s 1134269
 
5.0%
i 1065864
 
4.7%
Other values (50) 8340450
36.7%

COUNTY
Categorical

IMBALANCE  MISSING 

Distinct13
Distinct (%)< 0.1%
Missing66959
Missing (%)4.3%
Memory size11.9 MiB
Jackson
1165976 
Clay
248388 
Platte
 
80799
CLAY
 
573
Cass
 
228
Other values (8)
 
292

Length

Max length13
Median length7
Mean length6.4463307
Min length4

Characters and Unicode

Total characters9645361
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowJackson
2nd rowClay
3rd rowJackson
4th rowJackson
5th rowJackson

Common Values

ValueCountFrequency (%)
Jackson 1165976
74.6%
Clay 248388
 
15.9%
Platte 80799
 
5.2%
CLAY 573
 
< 0.1%
Cass 228
 
< 0.1%
JACKSON 219
 
< 0.1%
jackson 41
 
< 0.1%
clay 23
 
< 0.1%
platte 4
 
< 0.1%
Platte County 2
 
< 0.1%
Other values (3) 3
 
< 0.1%
(Missing) 66959
 
4.3%

Length

2024-02-28T04:34:24.740179image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jackson 1166236
77.9%
clay 248984
 
16.6%
platte 80806
 
5.4%
cass 228
 
< 0.1%
county 2
 
< 0.1%
clayl 1
 
< 0.1%
ackson 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 1495463
15.5%
s 1166474
12.1%
J 1166195
12.1%
c 1166041
12.1%
o 1166020
12.1%
n 1166020
12.1%
k 1166018
12.1%
l 329218
 
3.4%
C 249411
 
2.6%
y 248414
 
2.6%
Other values (16) 326087
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8146132
84.5%
Uppercase Letter 1499227
 
15.5%
Space Separator 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1495463
18.4%
s 1166474
14.3%
c 1166041
14.3%
o 1166020
14.3%
n 1166020
14.3%
k 1166018
14.3%
l 329218
 
4.0%
y 248414
 
3.0%
t 161612
 
2.0%
e 80805
 
1.0%
Other values (3) 47
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
J 1166195
77.8%
C 249411
 
16.6%
P 80802
 
5.4%
A 793
 
0.1%
L 574
 
< 0.1%
Y 573
 
< 0.1%
K 219
 
< 0.1%
S 219
 
< 0.1%
O 219
 
< 0.1%
N 219
 
< 0.1%
Other values (2) 3
 
< 0.1%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9645359
> 99.9%
Common 2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1495463
15.5%
s 1166474
12.1%
J 1166195
12.1%
c 1166041
12.1%
o 1166020
12.1%
n 1166020
12.1%
k 1166018
12.1%
l 329218
 
3.4%
C 249411
 
2.6%
y 248414
 
2.6%
Other values (15) 326085
 
3.4%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9645361
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1495463
15.5%
s 1166474
12.1%
J 1166195
12.1%
c 1166041
12.1%
o 1166020
12.1%
n 1166020
12.1%
k 1166018
12.1%
l 329218
 
3.4%
C 249411
 
2.6%
y 248414
 
2.6%
Other values (16) 326087
 
3.4%

POLICE DISTRICT
Categorical

MISSING 

Distinct6
Distinct (%)< 0.1%
Missing35265
Missing (%)2.3%
Memory size11.9 MiB
Metro
383130 
East
374326 
Central
234327 
Shoal Creek
209760 
South
201792 

Length

Max length11
Median length7
Mean length5.8854269
Min length4

Characters and Unicode

Total characters8992638
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMetro
2nd rowShoal Creek
3rd rowEast
4th rowEast
5th rowCentral

Common Values

ValueCountFrequency (%)
Metro 383130
24.5%
East 374326
23.9%
Central 234327
15.0%
Shoal Creek 209760
13.4%
South 201792
12.9%
North 124615
 
8.0%
(Missing) 35265
 
2.3%

Length

2024-02-28T04:34:25.031884image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T04:34:25.312869image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
metro 383130
22.0%
east 374326
21.5%
central 234327
13.5%
shoal 209760
12.1%
creek 209760
12.1%
south 201792
11.6%
north 124615
 
7.2%

Most occurring characters

ValueCountFrequency (%)
t 1318190
14.7%
e 1036977
11.5%
r 951832
10.6%
o 919297
10.2%
a 818413
9.1%
h 536167
 
6.0%
C 444087
 
4.9%
l 444087
 
4.9%
S 411552
 
4.6%
M 383130
 
4.3%
Other values (7) 1728906
19.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7045168
78.3%
Uppercase Letter 1737710
 
19.3%
Space Separator 209760
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1318190
18.7%
e 1036977
14.7%
r 951832
13.5%
o 919297
13.0%
a 818413
11.6%
h 536167
7.6%
l 444087
 
6.3%
s 374326
 
5.3%
n 234327
 
3.3%
k 209760
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
C 444087
25.6%
S 411552
23.7%
M 383130
22.0%
E 374326
21.5%
N 124615
 
7.2%
Space Separator
ValueCountFrequency (%)
209760
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8782878
97.7%
Common 209760
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1318190
15.0%
e 1036977
11.8%
r 951832
10.8%
o 919297
10.5%
a 818413
9.3%
h 536167
 
6.1%
C 444087
 
5.1%
l 444087
 
5.1%
S 411552
 
4.7%
M 383130
 
4.4%
Other values (6) 1519146
17.3%
Common
ValueCountFrequency (%)
209760
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8992638
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 1318190
14.7%
e 1036977
11.5%
r 951832
10.6%
o 919297
10.2%
a 818413
9.1%
h 536167
 
6.0%
C 444087
 
4.9%
l 444087
 
4.9%
S 411552
 
4.6%
M 383130
 
4.3%
Other values (7) 1728906
19.2%

PARCEL ID NO
Real number (ℝ)

SKEWED  ZEROS 

Distinct175953
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106754.49
Minimum0
Maximum9.9589235 × 108
Zeros35266
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size11.9 MiB
2024-02-28T04:34:25.661532image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3898
Q138358
median85391
Q3136854
95-th percentile228308
Maximum9.9589235 × 108
Range9.9589235 × 108
Interquartile range (IQR)98496

Descriptive statistics

Standard deviation2353178.5
Coefficient of variation (CV)22.042899
Kurtosis76590.509
Mean106754.49
Median Absolute Deviation (MAD)49221
Skewness250.90887
Sum1.6688022 × 1011
Variance5.5374489 × 1012
MonotonicityNot monotonic
2024-02-28T04:34:25.987832image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 35266
 
2.3%
12614 4942
 
0.3%
492 2754
 
0.2%
12926 1265
 
0.1%
128568 542
 
< 0.1%
99048 520
 
< 0.1%
98624 450
 
< 0.1%
142693 433
 
< 0.1%
12635 414
 
< 0.1%
127254 360
 
< 0.1%
Other values (175943) 1516269
97.0%
ValueCountFrequency (%)
0 35266
2.3%
1 28
 
< 0.1%
5 13
 
< 0.1%
6 1
 
< 0.1%
7 142
 
< 0.1%
10 30
 
< 0.1%
12 23
 
< 0.1%
14 3
 
< 0.1%
17 72
 
< 0.1%
18 42
 
< 0.1%
ValueCountFrequency (%)
995892354 1
 
< 0.1%
935159351 1
 
< 0.1%
675192432 4
< 0.1%
673306734 1
 
< 0.1%
672126721 1
 
< 0.1%
647706478 1
 
< 0.1%
647476476 1
 
< 0.1%
620486201 1
 
< 0.1%
485674857 1
 
< 0.1%
250613250 3
< 0.1%

LATITUDE
Real number (ℝ)

Distinct157747
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.985272
Minimum35.797519
Maximum39.35464
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.9 MiB
2024-02-28T04:34:26.318328image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum35.797519
5-th percentile38.915251
Q138.997002
median39.054552
Q339.109302
95-th percentile39.257015
Maximum39.35464
Range3.557121
Interquartile range (IQR)0.1123

Descriptive statistics

Standard deviation0.52951741
Coefficient of variation (CV)0.013582499
Kurtosis31.132856
Mean38.985272
Median Absolute Deviation (MAD)0.056051
Skewness-5.6446435
Sum60942361
Variance0.28038868
MonotonicityNot monotonic
2024-02-28T04:34:26.649942image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.797519 40610
 
2.6%
39.100387 4940
 
0.3%
39.014493 2469
 
0.2%
39.099123 1246
 
0.1%
39.074934 476
 
< 0.1%
39.285963 380
 
< 0.1%
39.031378 374
 
< 0.1%
39.100349 371
 
< 0.1%
39.307933 360
 
< 0.1%
39.074825 312
 
< 0.1%
Other values (157737) 1511677
96.7%
ValueCountFrequency (%)
35.797519 40610
2.6%
38.827084 1
 
< 0.1%
38.83482 5
 
< 0.1%
38.835025 6
 
< 0.1%
38.838219 29
 
< 0.1%
38.838324 5
 
< 0.1%
38.838331 34
 
< 0.1%
38.838343 3
 
< 0.1%
38.838587 8
 
< 0.1%
38.838841 4
 
< 0.1%
ValueCountFrequency (%)
39.35464 5
 
< 0.1%
39.354635 2
 
< 0.1%
39.354631 1
 
< 0.1%
39.354629 6
 
< 0.1%
39.354601 3
 
< 0.1%
39.354591 5
 
< 0.1%
39.354575 16
< 0.1%
39.353859 1
 
< 0.1%
39.353853 3
 
< 0.1%
39.353638 1
 
< 0.1%

LONGITUDE
Real number (ℝ)

Distinct126281
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-94.792875
Minimum-103.89058
Maximum-94.386375
Zeros0
Zeros (%)0.0%
Negative1563215
Negative (%)100.0%
Memory size11.9 MiB
2024-02-28T04:34:26.991656image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-103.89058
5-th percentile-94.645131
Q1-94.583575
median-94.55375
Q3-94.523262
95-th percentile-94.474424
Maximum-94.386375
Range9.504204
Interquartile range (IQR)0.060313

Descriptive statistics

Standard deviation1.4864643
Coefficient of variation (CV)-0.015681181
Kurtosis33.45319
Mean-94.792875
Median Absolute Deviation (MAD)0.030071
Skewness-5.9512036
Sum-1.4818164 × 108
Variance2.209576
MonotonicityNot monotonic
2024-02-28T04:34:27.341936image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-103.890579 40610
 
2.6%
-94.577919 4958
 
0.3%
-94.529933 2472
 
0.2%
-94.577983 1246
 
0.1%
-94.591904 481
 
< 0.1%
-94.576776 414
 
< 0.1%
-94.472954 378
 
< 0.1%
-94.540878 372
 
< 0.1%
-94.59448 370
 
< 0.1%
-94.588552 320
 
< 0.1%
Other values (126271) 1511594
96.7%
ValueCountFrequency (%)
-103.890579 40610
2.6%
-94.756681 1
 
< 0.1%
-94.756298 1
 
< 0.1%
-94.754146 1
 
< 0.1%
-94.752204 1
 
< 0.1%
-94.752202 1
 
< 0.1%
-94.752171 12
 
< 0.1%
-94.75217 2
 
< 0.1%
-94.752163 8
 
< 0.1%
-94.752122 2
 
< 0.1%
ValueCountFrequency (%)
-94.386375 26
< 0.1%
-94.386377 2
 
< 0.1%
-94.386465 1
 
< 0.1%
-94.386516 13
< 0.1%
-94.386648 12
< 0.1%
-94.386673 1
 
< 0.1%
-94.386677 8
 
< 0.1%
-94.386711 3
 
< 0.1%
-94.386814 2
 
< 0.1%
-94.386906 2
 
< 0.1%
Distinct82
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.9 MiB
2024-02-28T04:34:27.755358image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length33
Median length27
Mean length9.007958
Min length3

Characters and Unicode

Total characters14081375
Distinct characters47
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowProperty
2nd rowStreets
3rd rowProperty
4th rowProperty
5th rowParks & Recreation
ValueCountFrequency (%)
trash 373808
17.4%
property 232185
 
10.8%
violations 199740
 
9.3%
water 185164
 
8.6%
animal 98921
 
4.6%
streets 74919
 
3.5%
trees 73966
 
3.4%
animals 73694
 
3.4%
leak 71347
 
3.3%
nuisance 65646
 
3.0%
Other values (93) 703849
32.7%
2024-02-28T04:34:28.497558image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 1368856
 
9.7%
e 1281014
 
9.1%
a 1209121
 
8.6%
t 1099999
 
7.8%
s 1011493
 
7.2%
i 990455
 
7.0%
o 868987
 
6.2%
n 672691
 
4.8%
590024
 
4.2%
l 571371
 
4.1%
Other values (37) 4417364
31.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11341069
80.5%
Uppercase Letter 2092916
 
14.9%
Space Separator 590024
 
4.2%
Other Punctuation 57366
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1368856
12.1%
e 1281014
11.3%
a 1209121
10.7%
t 1099999
9.7%
s 1011493
8.9%
i 990455
8.7%
o 868987
7.7%
n 672691
 
5.9%
l 571371
 
5.0%
h 515296
 
4.5%
Other values (14) 1751786
15.4%
Uppercase Letter
ValueCountFrequency (%)
T 457894
21.9%
P 317558
15.2%
S 274981
13.1%
V 248665
11.9%
W 225868
10.8%
A 201518
9.6%
L 119789
 
5.7%
N 70214
 
3.4%
M 49609
 
2.4%
B 27456
 
1.3%
Other values (11) 99364
 
4.7%
Space Separator
ValueCountFrequency (%)
590024
100.0%
Other Punctuation
ValueCountFrequency (%)
& 57366
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13433985
95.4%
Common 647390
 
4.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1368856
 
10.2%
e 1281014
 
9.5%
a 1209121
 
9.0%
t 1099999
 
8.2%
s 1011493
 
7.5%
i 990455
 
7.4%
o 868987
 
6.5%
n 672691
 
5.0%
l 571371
 
4.3%
h 515296
 
3.8%
Other values (35) 3844702
28.6%
Common
ValueCountFrequency (%)
590024
91.1%
& 57366
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14081375
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 1368856
 
9.7%
e 1281014
 
9.1%
a 1209121
 
8.6%
t 1099999
 
7.8%
s 1011493
 
7.2%
i 990455
 
7.0%
o 868987
 
6.2%
n 672691
 
4.8%
590024
 
4.2%
l 571371
 
4.1%
Other values (37) 4417364
31.4%

CATEGORY2
Categorical

MISSING 

Distinct9
Distinct (%)< 0.1%
Missing1001657
Missing (%)64.1%
Memory size11.9 MiB
Recycling
205683 
Roadways
74919 
Pets
73694 
Buildings
73234 
Weeds
35304 
Other values (4)
98724 

Length

Max length9
Median length8
Mean length7.4557161
Min length4

Characters and Unicode

Total characters4186817
Distinct characters23
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBuildings
2nd rowRoadways
3rd rowBuildings
4th rowBuildings
5th rowBuildings

Common Values

ValueCountFrequency (%)
Recycling 205683
 
13.2%
Roadways 74919
 
4.8%
Pets 73694
 
4.7%
Buildings 73234
 
4.7%
Weeds 35304
 
2.3%
Sewer 32457
 
2.1%
Parking 29902
 
1.9%
Signals 26246
 
1.7%
Curbs 10119
 
0.6%
(Missing) 1001657
64.1%

Length

2024-02-28T04:34:28.838234image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T04:34:29.136161image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
recycling 205683
36.6%
roadways 74919
 
13.3%
pets 73694
 
13.1%
buildings 73234
 
13.0%
weeds 35304
 
6.3%
sewer 32457
 
5.8%
parking 29902
 
5.3%
signals 26246
 
4.7%
curbs 10119
 
1.8%

Most occurring characters

ValueCountFrequency (%)
e 414899
9.9%
c 411366
9.8%
i 408299
9.8%
n 335065
 
8.0%
g 335065
 
8.0%
l 305163
 
7.3%
s 293516
 
7.0%
R 280602
 
6.7%
y 280602
 
6.7%
a 205986
 
4.9%
Other values (13) 916254
21.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3625259
86.6%
Uppercase Letter 561558
 
13.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 414899
11.4%
c 411366
11.3%
i 408299
11.3%
n 335065
9.2%
g 335065
9.2%
l 305163
8.4%
s 293516
8.1%
y 280602
7.7%
a 205986
5.7%
d 183457
5.1%
Other values (7) 451841
12.5%
Uppercase Letter
ValueCountFrequency (%)
R 280602
50.0%
P 103596
 
18.4%
B 73234
 
13.0%
S 58703
 
10.5%
W 35304
 
6.3%
C 10119
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 4186817
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 414899
9.9%
c 411366
9.8%
i 408299
9.8%
n 335065
 
8.0%
g 335065
 
8.0%
l 305163
 
7.3%
s 293516
 
7.0%
R 280602
 
6.7%
y 280602
 
6.7%
a 205986
 
4.9%
Other values (13) 916254
21.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4186817
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 414899
9.9%
c 411366
9.8%
i 408299
9.8%
n 335065
 
8.0%
g 335065
 
8.0%
l 305163
 
7.3%
s 293516
 
7.0%
R 280602
 
6.7%
y 280602
 
6.7%
a 205986
 
4.9%
Other values (13) 916254
21.9%

CATEGORY3
Categorical

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing1404943
Missing (%)89.9%
Memory size11.9 MiB
Alleys
74919 
Construction
73234 
Ditch
10119 

Length

Max length12
Median length6
Mean length8.7123244
Min length5

Characters and Unicode

Total characters1378917
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConstruction
2nd rowAlleys
3rd rowConstruction
4th rowConstruction
5th rowConstruction

Common Values

ValueCountFrequency (%)
Alleys 74919
 
4.8%
Construction 73234
 
4.7%
Ditch 10119
 
0.6%
(Missing) 1404943
89.9%

Length

2024-02-28T04:34:29.464213image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T04:34:29.721194image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
alleys 74919
47.3%
construction 73234
46.3%
ditch 10119
 
6.4%

Most occurring characters

ValueCountFrequency (%)
t 156587
11.4%
l 149838
10.9%
s 148153
10.7%
o 146468
10.6%
n 146468
10.6%
c 83353
 
6.0%
i 83353
 
6.0%
A 74919
 
5.4%
e 74919
 
5.4%
y 74919
 
5.4%
Other values (5) 239940
17.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1220645
88.5%
Uppercase Letter 158272
 
11.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 156587
12.8%
l 149838
12.3%
s 148153
12.1%
o 146468
12.0%
n 146468
12.0%
c 83353
6.8%
i 83353
6.8%
e 74919
6.1%
y 74919
6.1%
r 73234
6.0%
Other values (2) 83353
6.8%
Uppercase Letter
ValueCountFrequency (%)
A 74919
47.3%
C 73234
46.3%
D 10119
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 1378917
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 156587
11.4%
l 149838
10.9%
s 148153
10.7%
o 146468
10.6%
n 146468
10.6%
c 83353
 
6.0%
i 83353
 
6.0%
A 74919
 
5.4%
e 74919
 
5.4%
y 74919
 
5.4%
Other values (5) 239940
17.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1378917
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 156587
11.4%
l 149838
10.9%
s 148153
10.7%
o 146468
10.6%
n 146468
10.6%
c 83353
 
6.0%
i 83353
 
6.0%
A 74919
 
5.4%
e 74919
 
5.4%
y 74919
 
5.4%
Other values (5) 239940
17.4%

Interactions

2024-02-28T04:33:46.281299image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:27.708898image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:31.545799image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:35.230234image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:38.925926image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:42.525206image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:46.840627image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:28.484907image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:32.096692image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:35.896465image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:39.550317image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:43.208742image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:47.415903image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:29.162942image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:32.690506image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:36.437176image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:40.164791image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:43.872605image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:47.905707image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:29.735270image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:33.324008image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:37.046520image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:40.751967image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:44.499601image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:48.461776image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:30.310369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:34.008080image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:37.655701image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:41.332761image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:45.111666image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:49.000914image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:30.900442image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:34.631891image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:38.329571image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:41.906078image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T04:33:45.733447image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-02-28T04:33:50.958645image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-28T04:33:55.854312image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CASE IDSOURCEDEPARTMENTWORK GROUPTYPEDETAILCREATION DATECREATION TIMESTATUSEXCEEDED EST TIMEFRAMECLOSED DATEDAYS TO CLOSESTREET ADDRESSZIP CODENEIGHBORHOODCOUNTYPOLICE DISTRICTPARCEL ID NOLATITUDELONGITUDECATEGORY1CATEGORY2CATEGORY3
02019119972PHONENHSNHS-Dangerous Buildings-Dangerous BuildingStandard06/24/201907:40 AMRESOLY11/19/2021879.02317 E 50th St64130.0Blue HillsJacksonMetro13886339.035489-94.557309PropertyBuildingsConstruction
12019207923WEBPublic WorksPublic Works-Street and Traffic-District 1CrackDistrict 112/22/201907:56 PMRESOLY06/26/2020187.010329 N Forest Ave64155.0New MarkClayShoal Creek10370339.281960-94.564453StreetsRoadwaysAlleys
22021005976PHONENHSNHS-Dangerous Buildings-Dangerous BuildingStandard01/19/202102:43 PMRESOLY11/15/2021300.02623 E 10th St64127.0Independence PlazaJacksonEast1070339.101272-94.549951PropertyBuildingsConstruction
32020149407PHONENHSNHS-Neighborhood Preservation-Property MaintenanceOther Property Issue11/25/202009:19 AMRESOLY04/26/2021152.02474 Chelsea Ave64127.0East Community Team SouthJacksonEast1917839.079895-94.526959PropertyBuildingsConstruction
42020054721WEBParks and RecParks and Rec-Central Region-Park MaintenanceCentral04/18/202005:10 PMRESOLN04/30/202012.0400 W 31st St64108.0Westside SouthJacksonCentral12856839.074934-94.591904Parks & RecreationNaNNaN
52019182182PHONENHSNHS-Dangerous Buildings-Dangerous BuildingStandard10/21/201910:29 AMRESOLY08/03/2020287.04043 Kenwood Ave64110.0South Hyde ParkJacksonCentral13329139.052983-94.577808PropertyBuildingsConstruction
62019184705WEBNHSNHS-Solid Waste-RecyclingMissed by City10/25/201910:02 AMRESOLN10/28/20193.0637 E 62nd St64110.0Western 49-63JacksonMetro10610639.014160-94.579673TrashRecyclingNaN
72019184590WEBParks and RecParks and Rec-Landscape Services-ForestryTrimmingTree Limbs10/25/201904:44 AMRESOLY12/04/201940.010901 Blue Ridge Blvd64134.0Ruskin HeightsJacksonSouth5954638.924085-94.507192TreesNaNNaN
82020095175PHONENHSNHS-Dangerous Buildings-Dangerous BuildingStandard07/13/202008:00 AMRESOLY11/29/2021504.04215 E 60th St64130.0Swope Parkway-ElmwoodJacksonMetro32639.016565-94.536611PropertyBuildingsConstruction
92015094486PHONENHSNHS-Solid Waste-AdministrationServicesService Issue / Problem08/07/201501:45 PMRESOLY05/27/2016294.04518 N Askew Ave64117.0Antioch AcresClayShoal Creek7694639.176846-94.537416TrashRecyclingNaN
CASE IDSOURCEDEPARTMENTWORK GROUPTYPEDETAILCREATION DATECREATION TIMESTATUSEXCEEDED EST TIMEFRAMECLOSED DATEDAYS TO CLOSESTREET ADDRESSZIP CODENEIGHBORHOODCOUNTYPOLICE DISTRICTPARCEL ID NOLATITUDELONGITUDECATEGORY1CATEGORY2CATEGORY3
15632052019175846PHONEParks and RecParks and Rec-Landscape Services-ForestryRemovalDeclining10/04/201911:35 AMRESOLY12/04/201961.03942 Wyandotte St64111.0Old WestportJacksonCentral13264439.055001-94.588843TreesNaNNaN
15632062019175727PHONEParks and RecParks and Rec-Landscape Services-ForestryRemovalTree Limbs10/04/201909:32 AMRESOLY11/05/201932.09014 E 88th Ter64138.0White OakJacksonSouth6640838.962712-94.478245TreesNaNNaN
15632072019176019PHONEParks and RecParks and Rec-Landscape Services-ForestryTrimmingBlock Pruning10/04/201903:51 PMRESOLN10/11/20197.09208 NE 109th Ct64157.0Shoal CreekClayShoal Creek20967839.289643-94.464390TreesNaNNaN
15632082019175967PHONEParks and RecParks and Rec-Landscape Services-ForestryTrimmingTree Limbs10/04/201902:46 PMRESOLY10/30/201926.03106 Peery Ave64127.0Independence PlazaJacksonEast1087639.099803-94.545545TreesNaNNaN
15632092019175920PHONEParks and RecParks and Rec-Landscape Services-ForestryTrimmingTree Limbs10/04/201901:04 PMRESOLY10/19/201915.05048 NE 56th Pl64119.0Ravenwood-SomersetClayShoal Creek9040839.195871-94.518434TreesNaNNaN
15632102019175624PHONENHSNHS-Solid Waste-Trash CollectionMissed by Contractor North10/03/201906:18 PMRESOLN10/05/20192.010415 NW 87th St64153.0KCI & 2nd CreekPlatteNorth21172439.251356-94.702155TrashRecyclingNaN
15632112019176099PHONEParks and RecParks and Rec-Central Region-Park MaintenanceCentral10/05/201908:13 AMRESOLN10/10/20195.02308 Topping Ave64127.0South Blue ValleyJacksonEast2011839.082634-94.515668Parks & RecreationNaNNaN
15632122019176030PHONENHSNHS-Solid Waste-Dead AnimalCurb10/04/201904:07 PMRESOLN10/05/20191.05437 South Benton Ave64130.0North Town Fork CreekJacksonMetro3626539.026897-94.551349AnimalsPetsNaN
15632132019176115PHONEPublic WorksPublic Works-Capital Projects-Traffic PermitsPlateMissing / Displaced10/05/201904:50 PMRESOLY12/04/201960.01201 W 75th St64114.0Ward ParkwayJacksonMetro12044438.992568-94.604029StreetsRoadwaysAlleys
15632142019172858WEBCity Planning and DevelopmentCity Planning and Development-Permit Compliance-Construction Issue/ConcernWork Without Permit09/27/201908:01 PMRESOLY10/06/20199.010799 NW Skyview Ave64154.0KCI & 2nd CreekPlatteNorth20775639.288525-94.651254PropertyBuildingsConstruction